| Dr. Richard Evans | Dr. Benjamin Soltoff | Ms. Ging Cee Ng (TA) | |
|---|---|---|---|
| [email protected] | [email protected] | [email protected] | |
| Office | 250 Saieh Hall | 249 Saieh Hall | 251 Saieh Hall |
| Office Hours | W 2:30-4:30pm | Th 2-4pm | Th 3-5pm |
| GitHub | rickecon | bensoltoff | gingcee |
- Meeting day/time: MW 11:30-12:50pm, Saieh Hall, Room 247
- Lab session: W 5-5:50pm, Saieh Hall, Room 021
- Office hours also available by appointment
- Grader: Reuben Bauer ([email protected])
Students are often well trained in the details of specific models relevant to their respective fields. This course presents a generic definition of a model in the social sciences as well as a taxonomy of the wide range of different types of models used. We then cover principles of model building, including static versus dynamic models, linear versus nonlinear, simple versus complicated, and identification versus overfitting. Major types of models implemented in this course include systems of nonlinear equations, linear and nonlinear regression, supervised learning (decision trees, random forests, support vector machines, etc.), and unsupervised learning. We will also explore the wide range of computational strategies used to estimate models from data and make statistical and causal inference. Students will study both good examples and bad examples of modeling and estimation.
| Assignment | Quantity | Points | Total Points | Percent |
|---|---|---|---|---|
| Problem Sets | 9 | 10 | 90 | 90% |
| Midterm exam | 1 | 10 | 10 | 10% |
| Total Points | -- | -- | 100 | 100% |
If you need any special accommodations, please provide us with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.
| Date | Day | Topic | Readings | Assignment |
|---|---|---|---|---|
| Jan. 4 | W | Model/theory building | V1997 | |
| Jan. 9 | M | Data generating process | PS1 | |
| Jan. 11 | W | Maximum likelihood estimation | Notes | |
| Jan. 16 | M | No class (Martin Luther King, Jr. Day) | ||
| Jan. 18 | W | |||
| Jan. 23 | M | Generalized method of moments | Notes | PS2 |
| Jan. 25 | W | |||
| Jan. 30 | M | Simulated method of moments | Notes | PS3 |
| Feb. 1 | W | |||
| Feb. 6 | M | Evans Midterm | PS4 | |
| Feb. 8 | W | Statistical learning and linear regression | ISL Ch 2-3 | |
| Feb. 13 | M | Logistic regression | ISL Ch 4.1-3 | Linear regression PS due |
| Feb. 15 | W | Generalized linear models | Notes | |
| Feb. 20 | M | Resampling methods (cross-validation and bootstrapping) | ISL Ch 5 | GLM PS due |
| Feb. 22 | W | Non-linear modeling | ISL Ch 7 | |
| Feb. 27 | M | Tree-based methods | ISL Ch 8 | Resampling/non-linear PS due |
| Mar. 1 | W | Support vector machines | ISL Ch 9 | |
| Mar. 6 | M | Non-parametric methods | TBD | Trees & SVM PS due |
| Mar. 8 | W | Unsupervised learning | ISL Ch 10 | |
| Mar. 15 | W | Nonparametric/unsupervised PS due |
All readings are required unless otherwise noted. Adjustments can be made throughout the quarter; be sure to check this repository frequently to make sure you know all the assigned readings.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. New York: Springer.
- VanderPlas, Jake. (2016). Python Data Science Handbook. O'Reilly Media, Inc.
- Varian, Hal R., "How to Build an Economic Model in Your Spare Time," in Passion and Craft: Economists at Work, eds. Michael Szenberg, University of Michigan Press, 1997.